Analysis of Complex Human Interactions in Unconstrained Videos

The literature in human activity recognition is very broad and many different approaches have been presented to interpret the content of a visual scene. In this thesis, we are interested in two-person interaction analysis in unconstrained videos. Specifically, we focus on two open issues:(1)discriminative patch segmentation,and (2) human interaction recognition. For the first problem, we introduce two models to extract discriminative patches of human interactions applied to different scenarios, namely, videos from surveillance cameras and videos in TV shows. For the other problem, we propose two different frameworks: (1) human interaction recognition using the self-similarity matrix, and (2) human interaction recognition using the multiple-instance-learning approach. Experimental results demonstrate the effectiveness of our methods.